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| [CourseClub.NET].url |
123B |
| [DesireCourse.Com].url |
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| 02. Empathize with the Computer.html |
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| 02. Why Neural Networks-zAkzOZntK6Y.mp4 |
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| 08. A3C Asynchronous Advantage Actor-Critic, N-step.html |
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| 08. Approaches to MARL.html |
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| 08. Arm Sample.html |
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| 08. Collaborate!.html |
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| 08. Work Experiences Accomplishments.html |
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| 08. Workspace.html |
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| 08. Workspace - Introduction.html |
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| 09. 10 Double DQN V2-PGCEMLujiGI.en.vtt |
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| 09. Advanced TicTacToe using AlphaZero.html |
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| 09. Collaborate!.html |
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| 09. M3 L5 09 A3C Asynchronous Advantage ActorCritic Parallel Training V2-kKRbAKhjACo.mp4 |
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| 09. M3L610 Almgren And Chriss Model SC V1-rokcEQ4LXbU.mp4 |
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| 09. M4 L2 C08 Cooperation Competition Mixed Environments A V1-vx6PIH5_oFg.en.vtt |
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| 09. MC Prediction - Solution Walkthrough-Pwiqk7Pncgc.en.vtt |
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| 09. Notebook Visualizing a Pooling Layer.html |
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| 09. Quiz Expected Sarsa.html |
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| 10. 10 Prioritized Experience Replay V1-cN8z-7Ze9L8.en.vtt |
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| 10. M3 L5 10 A3C Asynchronous Advantage ActorCritic Offpolicy Vs Onpolicy V1-AZiy5R0DESU.mp4 |
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| 11. Notebook Visualizing FashionMNIST.html |
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| 11. OpenAI Gym CliffWalkingEnv.html |
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| 11. Optimal Policies.html |
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| 11. Paper Description, Part 1.html |
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| 11. PPO part 2- Clipping Policy Updates.html |
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| 11. Regularization 2.html |
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| 11. The Efficient Frontier.html |
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| 11. Why Policy-Based Methods.html |
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| 12. A2C Code Walk-through.html |
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| 12. A2c Export V1-LiUBJje2N0c.mp4 |
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| 12. DRL for Optimal Execution of Portfolio Transactions.html |
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| 12. Dropout.html |
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| 12. Epsilon-Greedy Policies.html |
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| 12. Implement a Class.html |
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| 12. L612 Epsilon Greedy Policies RENDER V4-PxJMtlR06MY.en.vtt |
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| 12. M4 L2 C10b Paper Description Part II V2-4hFAhtLJR5U.en.vtt |
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| 12. Next Lesson.html |
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| 12. Outro.html |
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| 12. Paper Description, Part 2.html |
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| 12. PPO summary.html |
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| 12. Quiz Optimal Policies.html |
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| 12. Quiz Pole-Balancing.html |
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| 12. Reflect on your commit messages.html |
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| 12. Summary.html |
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| 12. TLPPO Summary V1-qRAUAAWA_kc.mp4 |
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| 12. Training in PyTorch.html |
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| 12. Up Next.html |
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| 12. Vector Storage.html |
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| 12. Workspace - Introduction.html |
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| 13. Anatomy of a Function.html |
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| 13. Class Variables.html |
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| 13. Coding Exercise.html |
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| 13. Exercise Vector Storage.html |
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| 13. GAE Generalized Advantage Estimation.html |
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| 13. Linear Function Approximation.html |
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| 13. MC Control.html |
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| 13. MDPs, Part 1.html |
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| 13. Mini Project DP (Part 2).html |
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| 13. Notebook Fashion MNIST Training Exercise.html |
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| 13. pong with PPO (code walkthrough).html |
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| 13. Summary.html |
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| 13. Summary.html |
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| 14. Class Function Declarations.html |
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| 14. DDPG Deep Deterministic Policy Gradient, Continuous Actions.html |
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| 14. Exploration vs. Exploitation.html |
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| 14. Interview with Art - Part 3.html |
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| 14. Kernel Functions.html |
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| 14. Lab Instructions.html |
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| 14. M3 L5 14 DDPG Deep Deterministic Policy Gradient Continuous Actionspace V1-0NVOPIyrr98.mp4 |
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| 14. MDPs, Part 2.html |
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| 14. Multiple Outputs.html |
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| 14. Notebook FashionMNIST, Solution 1.html |
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| 14. Policy Improvement.html |
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| 14. pong with PPO (workspace).html |
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| 14. References.html |
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| 14. Vanishing Gradient.html |
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| 14. Workspace.html |
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| 15. Analyzing Performance.html |
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| 15. Constructor Functions.html |
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| 15. DDPG Deep Deterministic Policy Gradient, Soft Updates.html |
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| 15. Exercise References.html |
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| 15. Implementation.html |
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| 15. M3 L5 15 DDPG Deep Deterministic Policy Gradient Soft Updates V1-RT-HDnAVe9o.mp4 |
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| 15. MADDPG - Lab.html |
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| 15. Non-Linear Function Approximation.html |
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| 15. Other Activation Functions.html |
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| 15. Quiz One-Step Dynamics, Part 1.html |
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| 15. Review Dropout.html |
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| 16. Notebook FashionMNIST, Solution 2.html |
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| sigmoid-derivative.gif |
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